5 resultados para FIELD ANALYSIS

em Universidade do Minho


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The authors thank the federal agency CAPES and the Foundation for Research Support of the state of Sao Paulo, Brazil (FAPESP) for providing a PhD scholarship, and the University of Minho, in Portugal, for the international collaboration.

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This paper analyzes the safety, environmental and occupational health of workers in the small construction industry in Brazil. In this sector there are still many unsafe practices, which are very common in small work sites. We used a qualitative approach to understand these problems by long interviews with people who work directly in small construction sites, including occupational physicians, civil engineers, safety engineers, safety technicians, general foremen, construction workers, labor unionists and auditors. This paper aims to demonstrate that the "invisibility" of the small sites workers makes them less safe and therefore more prone to accidents, also weakening their health. The results show that small constructions workers are less visible to society and supervision because of their short periods of work. Therefore, they are also uncovered to the rigorous applicability of principles of safety and accident prevention. Thus, it has been seen in this field of work a precarious application of NR - 18, which was specifically made for the construction sites and it needs simplification to meet normative characteristics of small construction sites. In the State of Rio de Janeiro, some laws on small sites were recently created and implemented. This study concludes that the rules to work are not being taken as seriously as the legislation determinates, remaining practically unknown by many professionals, from the plot command, supervisors, engineers, architects and technicians who work on construction sites. This ignorance creates space for the lack of safety and consequently to accidents, leading to by weakness in the workers health. Therefore, the work process needs to be modified, the safety regulation must be disseminated through safer practices, promoting employee health and ensure that the work of small sites can be visible, especially ensuring the construction workers health and safety.

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An empirical system was developed to obtain a quality index for rock slopes in road infrastructures, named Slope Quality Index (SQI), and it was applied to a set of real slopes.The SQI is supported in nine factors affecting slope stability that contemplate the evaluation of different parameters. Consequently, each factor is classified by the degree of importance and influence by assigned weights. These weights were established through a statistical analysis of replies to a survey that was distributed to several experienced professionals in the field. The proposed SQI varies between1 and 5, corresponding to slopes in very good and very bad condition state, respectively. Besides the advantage linked to a quantitative and qualitative evaluation of slopes, theSQI also allows identifying the most critical factors on the slope stability, which is a fundamental issue for an efficient management of the slope network in the road infrastructure, namely in the planning of conservation and maintenance operations.

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Dissertação de mestrado integrado em Engenharia e Gestão de Sistemas de Informação

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Recently, there has been a growing interest in the field of metabolomics, materialized by a remarkable growth in experimental techniques, available data and related biological applications. Indeed, techniques as Nuclear Magnetic Resonance, Gas or Liquid Chromatography, Mass Spectrometry, Infrared and UV-visible spectroscopies have provided extensive datasets that can help in tasks as biological and biomedical discovery, biotechnology and drug development. However, as it happens with other omics data, the analysis of metabolomics datasets provides multiple challenges, both in terms of methodologies and in the development of appropriate computational tools. Indeed, from the available software tools, none addresses the multiplicity of existing techniques and data analysis tasks. In this work, we make available a novel R package, named specmine, which provides a set of methods for metabolomics data analysis, including data loading in different formats, pre-processing, metabolite identification, univariate and multivariate data analysis, machine learning, and feature selection. Importantly, the implemented methods provide adequate support for the analysis of data from diverse experimental techniques, integrating a large set of functions from several R packages in a powerful, yet simple to use environment. The package, already available in CRAN, is accompanied by a web site where users can deposit datasets, scripts and analysis reports to be shared with the community, promoting the efficient sharing of metabolomics data analysis pipelines.